Photoplethysmogram (PPG) signals are widely used in healthcare for monitoring vital signs, but they are susceptible to motion artifacts that can lead to inaccurate interpretations. In this study, the use of label propagation techniques to propagate labels among PPG samples is explored, particularly in imbalanced class scenarios where clean PPG samples are significantly outnumbered by artifact-contaminated samples. With a precision of 91%, a recall of 90% and an F1 score of 90% for the class without artifacts, the results demonstrate its effectiveness in labeling a medical dataset, even when clean samples are rare. For the classification of artifacts our study compares supervised classifiers such as conventional classifiers and neural networks (MLP, Transformers, FCN) with the semi-supervised label propagation algorithm. With a precision of 89%, a recall of 95% and an F1 score of 92%, the KNN supervised model gives good results, but the semi-supervised algorithm performs better in detecting artifacts. The findings suggest that the semi-supervised algorithm label propagation hold promise for artifact detection in PPG signals, which can enhance the reliability of PPG-based health monitoring systems in real-world applications.

翻译：光电容积脉搏波（PPG）信号广泛应用于医疗健康领域以监测生命体征，但易受运动伪影干扰，导致解读结果不准确。本研究探索了标签传播技术在PPG样本间传播标签的应用，特别是在类别不平衡场景下——其中无伪影的干净PPG样本数量显著少于受伪影污染的样本。针对无伪影类别，该方法取得了91%的精确率、90%的召回率和90%的F1分数，结果表明即使在干净样本稀少的情况下，该方法在医学数据集标注中仍具有有效性。在伪影分类方面，本研究比较了监督分类器（如传统分类器和神经网络：MLP、Transformer、FCN）与半监督标签传播算法的性能。KNN监督模型以89%的精确率、95%的召回率和92%的F1分数取得了良好效果，但半监督算法在伪影检测中表现更优。研究结果表明，半监督标签传播算法在PPG信号伪影检测中具有应用潜力，可提升基于PPG的健康监测系统在真实场景中的可靠性。